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体验新版 GitCode,发现更多精彩内容 >>
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0443c6f4
编写于
3月 24, 2022
作者:
X
xiayanming
提交者:
GitHub
3月 24, 2022
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电子邮件补丁
差异文件
[Auto Parallel] Gradient merge pass support dist attribute (#40737)
* [Auto Parallel] gradient merge pass support dist attribute
上级
a8f86600
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
45 addition
and
18 deletion
+45
-18
python/paddle/distributed/passes/auto_parallel_gradient_merge.py
...paddle/distributed/passes/auto_parallel_gradient_merge.py
+30
-11
python/paddle/fluid/tests/unittests/distributed_passes/test_dist_gradient_merge_pass.py
...tests/distributed_passes/test_dist_gradient_merge_pass.py
+15
-7
未找到文件。
python/paddle/distributed/passes/auto_parallel_gradient_merge.py
浏览文件 @
0443c6f4
...
...
@@ -22,6 +22,10 @@ from paddle.fluid.framework import program_guard, device_guard
from
paddle.fluid
import
unique_name
,
layers
from
paddle.fluid.clip
import
append_gradient_clip_ops
from
.pass_base
import
PassBase
,
PassType
,
register_pass
from
paddle.distributed.auto_parallel.utils
import
set_var_dist_attr
from
paddle.distributed.auto_parallel.utils
import
naive_set_dist_op_attr_for_program_by_mesh_and_mapping
from
paddle.distributed.auto_parallel.process_group
import
get_world_process_group
world_process_group
=
get_world_process_group
()
def
_is_the_backward_op
(
op
):
...
...
@@ -68,15 +72,11 @@ def _remove_and_get_optimizer_op(main_program, dist_context):
def
_remove_op_role_var
(
param
,
grad
):
op_maker
=
core
.
op_proto_and_checker_maker
op
=
grad
.
op
assert
_is_the_backward_op
(
op
),
\
'grad.op={} is not the backward op which produces the grad={}'
\
.
format
(
op
,
grad
.
name
)
if
op
.
has_attr
(
op_maker
.
kOpRoleVarAttrName
()):
op
.
_remove_attr
(
op_maker
.
kOpRoleVarAttrName
())
def
_get_gm_cond_var
(
main_program
,
k_steps
):
def
_get_gm_cond_var
(
main_program
,
k_steps
,
dist_context
):
main_block
=
main_program
.
global_block
()
# Add const var
k_step_var
=
layers
.
create_global_var
(
...
...
@@ -86,6 +86,7 @@ def _get_gm_cond_var(main_program, k_steps):
dtype
=
'int32'
,
persistable
=
True
,
force_cpu
=
True
)
set_var_dist_attr
(
dist_context
,
k_step_var
,
[
-
1
],
world_process_group
.
ranks
)
zero_var
=
layers
.
create_global_var
(
name
=
"gradient_merge_zero"
,
...
...
@@ -94,6 +95,7 @@ def _get_gm_cond_var(main_program, k_steps):
dtype
=
'int32'
,
persistable
=
True
,
force_cpu
=
True
)
set_var_dist_attr
(
dist_context
,
zero_var
,
[
-
1
],
world_process_group
.
ranks
)
# Add step var & cond var
step_var
=
layers
.
create_global_var
(
...
...
@@ -103,6 +105,7 @@ def _get_gm_cond_var(main_program, k_steps):
dtype
=
'int32'
,
persistable
=
True
,
force_cpu
=
True
)
set_var_dist_attr
(
dist_context
,
step_var
,
[
-
1
],
world_process_group
.
ranks
)
cond_var
=
layers
.
create_global_var
(
name
=
"gradient_merge_cond"
,
...
...
@@ -111,24 +114,29 @@ def _get_gm_cond_var(main_program, k_steps):
dtype
=
'bool'
,
persistable
=
False
,
force_cpu
=
True
)
set_var_dist_attr
(
dist_context
,
cond_var
,
[
-
1
],
world_process_group
.
ranks
)
with
device_guard
(
"cpu"
):
# step_var = (step_var + 1) % k_step
layers
.
increment
(
x
=
step_var
,
value
=
1.0
,
in_place
=
True
)
main_block
.
append_op
(
elementwise_mod_op
=
main_block
.
append_op
(
type
=
'elementwise_mod'
,
inputs
=
{
'X'
:
step_var
,
'Y'
:
k_step_var
},
outputs
=
{
'Out'
:
step_var
},
attrs
=
{
'axis'
:
-
1
,
'use_mkldnn'
:
False
})
naive_set_dist_op_attr_for_program_by_mesh_and_mapping
(
elementwise_mod_op
,
world_process_group
.
ranks
,
[
-
1
],
dist_context
)
# cond_var = (step_var == 0)
main_block
.
append_op
(
equal_op
=
main_block
.
append_op
(
type
=
'equal'
,
inputs
=
{
'X'
:
step_var
,
'Y'
:
zero_var
},
outputs
=
{
'Out'
:
cond_var
})
naive_set_dist_op_attr_for_program_by_mesh_and_mapping
(
equal_op
,
world_process_group
.
ranks
,
[
-
1
],
dist_context
)
return
cond_var
...
...
@@ -137,7 +145,8 @@ def _append_gradient_merge_backward_op(
main_program
,
startup_program
,
params_grads
:
List
[
Tuple
[
Any
,
Any
]],
cond_var_name
:
str
)
->
Tuple
[
List
[
Tuple
[
Any
,
Any
]],
Dict
[
str
,
Any
]]:
cond_var_name
:
str
,
dist_context
)
->
Tuple
[
List
[
Tuple
[
Any
,
Any
]],
Dict
[
str
,
Any
]]:
main_block
=
main_program
.
global_block
()
startup_block
=
startup_program
.
global_block
()
...
...
@@ -156,12 +165,19 @@ def _append_gradient_merge_backward_op(
param_name
=
param
.
name
param_var
=
main_block
.
var
(
param_name
)
assert
(
param_var
is
not
None
)
ref_dist_attr
=
dist_context
.
get_tensor_dist_attr_for_program
(
param_var
)
assert
ref_dist_attr
is
not
None
gradient_merge_var
=
main_block
.
create_var
(
name
=
param_name
+
"@GRAD@GradientMerge"
,
shape
=
param_var
.
shape
,
dtype
=
param_var
.
dtype
,
persistable
=
True
)
param_to_gradient_merge
[
param_name
]
=
gradient_merge_var
ref_process_mesh
=
ref_dist_attr
.
process_mesh
ref_dims_mapping
=
ref_dist_attr
.
dims_mapping
set_var_dist_attr
(
dist_context
,
gradient_merge_var
,
ref_dims_mapping
,
ref_process_mesh
)
startup_gradient_merge_var
=
startup_block
.
create_var
(
name
=
param_name
+
"@GRAD@GradientMerge"
,
...
...
@@ -186,6 +202,8 @@ def _append_gradient_merge_backward_op(
attrs
=
{
'axis'
:
-
1
,
'use_mkldnn'
:
False
})
new_params_to_grads
.
append
([
param
,
gradient_merge_var
])
naive_set_dist_op_attr_for_program_by_mesh_and_mapping
(
new_grad_op
,
ref_process_mesh
,
ref_dims_mapping
,
dist_context
)
return
new_params_to_grads
,
param_to_gradient_merge
...
...
@@ -240,7 +258,7 @@ def _create_cond_block_and_update_optimizer(
new_op_desc
.
remove_attr
(
op_maker
.
kOpRoleVarAttrName
())
# op's update Grad
if
new_op_desc
.
input
(
"Grad"
):
if
core
.
grad_var_suffix
()
in
new_op_desc
.
input_arg_names
(
):
grad_value
=
new_op_desc
.
input
(
"Grad"
)[
0
]
# TODO FIXME(xym) support fp16
grad_merge_value
=
grad_value
+
'@GradientMerge'
...
...
@@ -265,7 +283,7 @@ def _create_cond_block_and_update_optimizer(
def
parse_program
(
main_program
,
startup_program
,
params_grads
,
k_steps
,
avg
,
dist_context
):
# 1 create gradient_merge_cond
cond_var
=
_get_gm_cond_var
(
main_program
,
k_steps
)
cond_var
=
_get_gm_cond_var
(
main_program
,
k_steps
,
dist_context
)
# 2 remove optimizer_op from main_program
optimize_ops_desc
=
_remove_and_get_optimizer_op
(
main_program
,
dist_context
)
...
...
@@ -275,7 +293,8 @@ def parse_program(main_program, startup_program, params_grads, k_steps, avg,
# 3 append gradient merge backward op to main_program
new_params_to_grads
,
param_to_gradient_merge
=
_append_gradient_merge_backward_op
(
main_program
,
startup_program
,
params_grads
,
cond_var
.
name
)
main_program
,
startup_program
,
params_grads
,
cond_var
.
name
,
dist_context
)
# 4 create ConditionalBlock and append gradient merge optimizer ops
_create_cond_block_and_update_optimizer
(
...
...
python/paddle/fluid/tests/unittests/distributed_passes/test_dist_gradient_merge_pass.py
浏览文件 @
0443c6f4
...
...
@@ -31,6 +31,7 @@ from paddle.fluid.initializer import NumpyArrayInitializer
from
paddle.distributed.passes
import
new_pass
,
PassManager
,
PassContext
import
paddle.distributed.fleet
as
fleet
from
dist_pass_test_base
import
DistPassTestBase
from
paddle.distributed.auto_parallel.dist_context
import
DistributedContext
logging
.
getLogger
().
setLevel
(
logging
.
INFO
)
paddle
.
enable_static
()
...
...
@@ -111,14 +112,20 @@ class TestGradientMergePass(DistPassTestBase):
def
init
(
self
):
self
.
_params_grads
=
None
self
.
_config
=
{
"k_steps"
:
4
,
"avg"
:
True
}
#self._config["dist_context"] = DistributedContext()
def
apply_passes
(
self
,
main_prog
,
startup_prog
):
self
.
_config
[
"params_grads"
]
=
self
.
_params_grads
pass_context
=
PassContext
()
auto_parallel_gradient_merge_pass
=
new_pass
(
"auto_parallel_gradient_merge_pass"
,
self
.
_config
)
auto_parallel_gradient_merge_pass
.
apply
([
main_prog
],
[
startup_prog
],
pass_context
)
#self._config["params_grads"] = self._params_grads
#pass_context = PassContext()
#auto_parallel_gradient_merge_pass = new_pass(
# "auto_parallel_gradient_merge_pass", self._config)
#auto_parallel_gradient_merge_pass.apply([main_prog], [startup_prog],
# pass_context)
dist_strategy
=
fleet
.
DistributedStrategy
()
dist_strategy
.
gradient_merge
=
True
dist_strategy
.
gradient_merge_configs
=
{
"k_steps"
:
4
,
"avg"
:
True
}
dist_strategy
.
semi_auto
=
True
fleet
.
init
(
is_collective
=
True
,
strategy
=
dist_strategy
)
def
test_result
(
self
):
no_pass_rets
=
self
.
_distributed_launch
(
...
...
@@ -135,7 +142,7 @@ class TestGradientMergePass(DistPassTestBase):
gradient_merge
=
True
,
batch_size
=
8
,
max_step
=
8
)
"""
# avg loss for gradient_merge pass
avg_loss = 0
pass_avg_ret_list = []
...
...
@@ -156,6 +163,7 @@ class TestGradientMergePass(DistPassTestBase):
rtol=self.rtol,
atol=self.atol,
equal_nan=self.equal_nan))
"""
def
get_model
(
self
,
place
,
gradient_merge
,
batch_size
,
max_step
):
paddle
.
seed
(
2021
)
...
...
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